Engineering the oil binding capacity and crystallinity of self-assembled fibrillar networks of 12-hydroxystearic acid in edible oils
Bibliographic record
Abstract
The crystallinity and oil binding capacity of 12-hydroxystearic acid (12HSA)-vegetable oil organogels was modified by changing the post-crystallization annealing temperature from 5 °C to 30 °C for 24 h. The gels stored at 5 °C had a highly branched crystalline structure with small uniform pores, as determined by cryo-scanning electron microscopy. Large T2proton relaxation peaks at 50 to 70 ms determined by pulse nuclear magnetic resonance (pNMR) suggested the presence of highly immobilized oil at 5 °C. When the gels were stored at 30 °C, longer fibers and a less branched network were observed. At 30 °C, the 12HSA network's crystallinity was enhanced with fewer inclusions of liquid oil as determined by pNMR. When the gels were stored at 30 °C, a significantly shorter T2 relaxation peak was observed. The increased crystallinity, at 30 °C, was attributed to a reduction in bulk supersaturation, resulting in a very high crystallographic mismatch nucleation barrier (ΔG*) which favored one-dimensional fiber growth. However, at a lower crystallization temperature of 5 °C, there is an increase in the supersaturation and hence the crystallographic mismatch barrier is significantly lower, increasing fiber tip branching. The nucleation-growth-branching-growth model for self-assembled fibrillar networks explains the differences in crystallinity, pore size and oil syneresis observed for the 12HSA-vegetable oil organogels. It was found that the gels stored at 30 °C syneresised 1.35 times faster than the gels stored at 5 °C. Furthermore, the change in the T2 relaxations and the ratio of the complex viscosity/pore radius were 1.35 and 1.30 respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".